Similarity search enhances access control systems for autonomous vehicles by enabling efficient comparison of data patterns to verify identities, detect anomalies, and enforce security policies. In these systems, every access request—whether from a user, device, or software component—generates data that can be compared against known valid or malicious patterns. By calculating similarity scores between incoming requests and stored reference data, the system can make real-time decisions about granting or denying access. For example, a user attempting to unlock a vehicle via facial recognition might have their facial data compared to stored profiles, while a software update request might be checked against known secure code signatures.
One practical application is anomaly detection in access logs. Autonomous vehicles generate logs of access attempts, such as login requests, API calls, or sensor data interactions. Similarity search can identify suspicious patterns by comparing new entries to historical records of attacks or policy violations. Suppose a hacker tries to brute-force access to the vehicle’s control system by sending repeated login attempts with slight variations. A similarity-based model could detect that these attempts share structural similarities with past brute-force attacks, such as rapid request intervals or incremental username changes, and block them. Similarly, geolocation data from access requests could be compared to a driver’s typical usage patterns—if a request originates from an unusual location, the system might require additional authentication.
Another use case is in verifying software integrity. Autonomous vehicles rely on numerous software components, and updates must be validated to prevent malicious code injection. Similarity search can compare cryptographic hashes or code snippets of incoming updates to trusted versions. For instance, if an over-the-air update claims to be a navigation module patch, the system could check whether the code’s structure and behavior align with previous legitimate updates. Even minor deviations in code similarity might trigger a security review. Similarly, for biometric authentication, a driver’s face or voice sample could be compared to stored templates using algorithms like cosine similarity, ensuring only authorized users gain access. This approach balances security with usability, as it adapts to natural variations in biometric data (e.g., lighting changes in facial recognition) while rejecting clearly mismatched inputs.